用灵活验证器提升大模型推理准确率与效率
Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier
- 设计可动态分配资源的生成式验证器，兼顾速度与精度
- 在数学推理数据集上优于自一致性等基线方法
- 适合需要高效高可靠推理的部署场景
大语言模型在复杂任务中的推理存在准确率与计算效率的权衡。验证环节虽旨在提升性能，却引入新挑战：复杂的生成式奖励模型（GenRM）在测试时直接集成会带来高昂计算成本，而简单方法又缺乏可靠性。为此，我们提出FlexiVe，一种可灵活分配验证预算的生成式验证器，实现快速可靠与精细严谨推理的动态平衡。进一步提出Solve-Detect-Verify框架，智能识别解题完成点，触发针对性验证并提供反馈。实验表明，FlexiVe在ProcessBench上精准定位推理错误；在AIME 2024、AIME 2025和CNMO等数学推理基准上，整体方法在推理准确率和推理效率上均超越自一致性等基线。系统提供了测试时高效增强大模型推理的可扩展方案。
原文摘要 · Abstract (English)
Large Language Model (LLM) reasoning for complex tasks inherently involves a trade-off between solution accuracy and computational efficiency. The subsequent step of verification, while intended to improve performance, further complicates this landscape by introducing its own challenging trade-off: sophisticated Generative Reward Models (GenRMs) can be computationally prohibitive if naively integrated with LLMs at test-time, while simpler, faster methods may lack reliability. To overcome these challenges, we introduce FlexiVe, a novel generative verifier that flexibly balances computational resources between rapid, reliable fast thinking and meticulous slow thinking using a Flexible Allocation of Verification Budget strategy. We further propose the Solve-Detect-Verify pipeline, an efficient inference-time scaling framework that intelligently integrates FlexiVe, proactively identifying solution completion points to trigger targeted verification and provide focused solver feedback. Experiments show FlexiVe achieves superior accuracy in pinpointing errors within reasoning traces on ProcessBench. Furthermore, on challenging mathematical reasoning benchmarks (AIME 2024, AIME 2025, and CNMO), our full approach outperforms baselines like self-consistency in reasoning accuracy and inference efficiency. Our system offers a scalable and effective solution to enhance LLM reasoning at test time.
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